Electronic exchanges have revolutionized how securities are digitally traded. Traditional trading pits and floors have been replaced by electronic and algorithmic trading, enabling everyday persons to become market participants. Faster networking speeds through financial engineering have enabled the creation of automated model-based trading algorithms (Kim, 2007, “Electronic and Algorithmic Trading Technology: The Complete Guide,” Elsevier Inc., print). However, algorithmic trading has prohibited typical market participants from capitalizing on potential revenues.
To consider the depth and complexity of algorithmic trading, consider high frequency trading. With high frequency trading, when a market participant places an order for a security, that order must be communicated through a variety of trading intermediaries, such as a brokerage firm, as well as various technical intermediaries, including a trading platform interface. Each intermediate communication requires an internal delay, typically in the form of data transfers and communications between systems, which naturally results in a corresponding latency. This latency extends a total time required to execute the order. When latency occurs in executing an order, the price of the security when the order is executed changes and, more likely than not, does not equal the price of the security when the order was initiated, creating a price transformation discrepancy. This price transformation discrepancy exists because market prices adjust in real time, yet orders must be communicated through a variety of channels, each of which takes a fraction of second to execute. Brokers and intermediate agents are able to capitalize and profit off the price transformation discrepancy of an order, potentially earning approximately 0.001 USD per order. While this potential earning appears insignificant, these fractions of a penny accumulate over multitudes of executed orders.
According to Bloomberg, high frequency trades accounted for approximately sixty percent of all U.S. equity volume in 2010. See, the Internet at bloomberg.com/news/articles/2013-06-06/how-the-robots-lost-high-frequency-tradings-rise-and-fall, accessed Feb. 20, 2018. However, these high frequency trades do not act as market participants. Thus, the added value to a market is negligible. They are often seen as a detriment because their participation does not directly affect the given market prices.
Limiting high frequency trading has been theorized but never attempted on mass scale. One proposed limitation to high frequency trading is to require that such trades traverse across a coil of cable approximately thirty-five miles in length. Such a cable would slow the time required to communicate quotes and trades to and/or from market participants by a standard time period of approximately 350 milliseconds thereby putting high frequency traders on the same playing field as conventional traders. The drawback with such approaches to ensuring that order executions occur over a standard time period, is that they only protect market participants from a fraction of a variety of latency related problems. For instance, such approaches do not address other existing latency discrepancies between the market participant and the exchange.
Given the above background, what is needed in the art are improved, more efficient, ways to interpret exchange data packets communicated between a market participant and an electronic exchange.
The information disclosed in this Background of the Invention section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.